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Reseach Article

An Approach based on Run Length Count for Denoising the Kannada Characters

by Karthik S, Mamatha H.r, Srikanta Murthy K
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 50 - Number 18
Year of Publication: 2012
Authors: Karthik S, Mamatha H.r, Srikanta Murthy K
10.5120/7875-1192

Karthik S, Mamatha H.r, Srikanta Murthy K . An Approach based on Run Length Count for Denoising the Kannada Characters. International Journal of Computer Applications. 50, 18 ( July 2012), 42-46. DOI=10.5120/7875-1192

@article{ 10.5120/7875-1192,
author = { Karthik S, Mamatha H.r, Srikanta Murthy K },
title = { An Approach based on Run Length Count for Denoising the Kannada Characters },
journal = { International Journal of Computer Applications },
issue_date = { July 2012 },
volume = { 50 },
number = { 18 },
month = { July },
year = { 2012 },
issn = { 0975-8887 },
pages = { 42-46 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume50/number18/7875-1192/ },
doi = { 10.5120/7875-1192 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:48:41.813986+05:30
%A Karthik S
%A Mamatha H.r
%A Srikanta Murthy K
%T An Approach based on Run Length Count for Denoising the Kannada Characters
%J International Journal of Computer Applications
%@ 0975-8887
%V 50
%N 18
%P 42-46
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Optical Character Recognition (OCR) is one of the important fields in image processing and pattern recognition domain. OCR with high accuracy finds application in offices, banks, healthcare etc. The accuracy of the OCR is primarily dependent on the quality of the input image. So, to achieve high accuracy OCR we should provide a high quality image, which is free from different types of noises, degradation, skews etc. In this paper, we have made an attempt to remove the noise, which is present in the input image. A novel method based on run length count is proposed to denoise the images. In this approach first the noisy image is binarized. Based on the horizontal and vertical run length count, the noise in the image will be identified and eliminated. The algorithm is tested with noisy epigraphical document images, noisy printed document images. The effectiveness of the algorithm is verified with images having synthetic noise derived from Gaussian, Speckle and Poisson noise models. The experimental results show that the proposed method is efficient for noise elimination.

References
  1. Doreswamy, K. Srikanta Murthy, G. Hemantha Kumar, P. Nagabushan, "Filtering Technique based on Minimum Majority Function to eliminate noises from the Epigraphical Script Images", National Conference on Recent Trends in Information Technology, 2002, page no 35-39
  2. Mukesh Motwani, Mukesh Gadiya, Rakhi Motwani, and Frederick C. Harris, Jr. A Survey of Image Denoising Techniques , Proceedings of GSPx 2004
  3. S. Grace Chang, Bin Yu, Martin Vetterli, "Adaptive Wavelet Thresholding for Image Denoising and Compression", IEEE Transactions On Image Processing, VOL. 9, NO. 9, September 2000, page no: 1532-1546
  4. Lakhwinder Kaur, Savita Gupta, R. C. Chauhan," Image Denoising using Wavelet Thresholding", Indian Conference On Computer Vision, Graphics And Image Processing, 2002
  5. Mohsen Ghazel, George H. Freeman, and Edward R. Vrscay, "Fractal-Wavelet Image Denoising Revisited", IEEE Transactions On Image Processing, VOL. 15, NO. 9, September 2006, page no: 2669-2675
  6. Jean-Luc Starck, Emmanuel J. Candès, and David L. Donoho, "The Curvelet Transform for Image Denoising", IEEE Transactions On Image Processing, VOL. 11, NO. 6, June 2002, page no: 670–684
  7. JIANG Tao?ZHAO Xin, "Research And Application Of Image Denoising Method Based On Curvelet Transform", The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B2, 2008, page no: 363-367
  8. S. Satheesh, Dr. KVSVR Prasad, "Medical Image Denoising Using Adaptive Threshold Based On Contourlet Transform", Advanced Computing: An International Journal, Vol. 2, No. 2, March 2011, page no: 52-58
  9. Tanzila Saba, Amjad Rehman And Ghazali Sulong, " An Intelligent Approach To Image Denoising", Journal of Theoretical and Applied Information Technology, page no: 32-36
  10. G. Vijaya, V. Vasudevan," Image Denoising Based On Soft Computing Techniques ", International Journal of Research and Reviews in Applied Sciences, April 2011, page no: 32-37
  11. Lei Zhang, WeishengDong, DavidZhang, GuangmingShi, "Two-stage image denoising by principal component analysis with local pixel grouping", Pattern Recognition 43, 2010, page no: 1531–1549
  12. Antoni Buades, Bartomeu Coll, Jean-Michel Morel, "A non-local algorithm for image denoising", IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Volume 2, 2005, page no: 60–65
  13. Buades A, B. Coll, J. M. Morel, "A Review Of Image Denoising Algorithms, With A New One", Society for Industrial and Applied Mathematics Multiscale Model. Simulation, Vol. 4, No. 2,2005, page no: 490–530
  14. Mamatha H R, Karthik S, Srikanta Murthy K, "Feature based recognition of handwritten Kannada numerals — A comparative study", IEEE International Conference on Computing, Communication and Applications, 2012
  15. Otsu, N. , "A Threshold Selection Method from Gray-Level Histograms," IEEE Transactions on Systems, Man, and Cybernetics, Vol. 9, No. 1, 1979, pp. 62-66.
Index Terms

Computer Science
Information Sciences

Keywords

Optical Character Recognition Image Denoising Run Length Count Epigraphical Document Printed Document Gaussian Noise Speckle Noise Poisson Noise